β-Lactam Therapeutic Drug Monitoring in Critically Ill Patients
Bibliographic record
Abstract
To the Editor—We write in response to the systematic review by Mangalore et al [1] on β-lactam therapeutic drug monitoring (TDM) in critically ill patients. With the ever-growing interest in TDM, and varying opinions on its utility, we commend the authors’ endeavor to report on patient important clinical outcomes. Their cumulative results highlight the largest barrier to implementation: no demonstrable benefit toward patient mortality or length of stay. Around the same time, Ewoldt et al [2] published the DOLPHIN trial, a randomized controlled trial comparing model-informed precision dosing of β-lactams and ciprofloxacin to standard dosing in critically ill patients. They also reported an insignificant benefit of TDM on intensive care unit length of stay and morality. To summarize the findings of both recent studies, we recreated the mortality meta-analysis from Mangalore et al and included the new β-lactam-specific data from Ewoldt et al. Data from studies included in the meta-analysis by Mangalore et al were extracted. The β-lactam data by Ewoldt et al (personal communication, T. Ewoldt, 6 February 2023) were added to the data file and reanalyzed (Figure 1) using random effects meta-analysis modeling on SPSS version 29 software.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".